Systems and Methods for Training a Machine-Learning-Based Monocular Depth Estimator

2. The system of claim 1, wherein the instructions in the training module to supervise training of the machine-learning-based monocular depth estimator include further instructions to compute a supervised depth loss between the predicted depth map and the ground-truth depth map.

3. The system of claim 1, wherein the instructions in the training module to supervise training of the machine-learning-based monocular depth estimator include instructions to compute the surface-normal loss as a cosine similarity function.

4. The system of claim 1, wherein the ground-truth depth map and the predicted depth map represent respective distances, from a camera, of the pixels in the virtual image as grayscale intensities.

5. The system of claim 1, wherein the training module includes instructions to train the machine-learning-based monocular depth estimator for deployment in one of an autonomous vehicle, a semi-autonomous vehicle, an Advanced Driver-Assistance System, a search and rescue robot, an aerial drone, and an indoor robot.

6. The system of claim 1, wherein the machine-learning-based monocular depth estimator includes at least one neural network.

7. The system of claim 1, wherein the virtual image is a Red-Green-Blue (RGB) image.

9. The non-transitory computer-readable medium of claim 8, wherein the instructions to supervise training of the machine-learning-based monocular depth estimator include further instructions to compute a supervised depth loss between the predicted depth map and the ground-truth depth map.

10. The non-transitory computer-readable medium of claim 8, wherein the instructions to supervise training of the machine-learning-based monocular depth estimator include instructions to compute the surface-normal loss as a cosine similarity function.

11. The non-transitory computer-readable medium of claim 8, wherein the ground-truth depth map and the predicted depth map represent respective distances, from a camera, of the pixels in the virtual image as grayscale intensities.

12. The non-transitory computer-readable medium of claim 8, wherein the instructions include instructions to train the machine-learning-based monocular depth estimator for deployment in one of an autonomous vehicle, a semi-autonomous vehicle, an Advanced Driver-Assistance System, a search and rescue robot, an aerial drone, and an indoor robot.

13. The non-transitory computer-readable medium of claim 8, wherein the machine-learning-based monocular depth estimator includes at least one neural network.

15. The method of claim 14, wherein supervising training of the machine-learning-based monocular depth estimator further includes computing a supervised depth loss between the predicted depth map and the ground-truth depth map.

16. The method of claim 14, wherein the surface-normal loss is computed as a cosine similarity function.

17. The method of claim 14, wherein the ground-truth depth map and the predicted depth map represent respective distances, from a camera, of the pixels in the virtual image as grayscale intensities.

18. The method of claim 14, wherein the machine-learning-based monocular depth estimator is trained for deployment in one of an autonomous vehicle, a semi-autonomous vehicle, an Advanced Driver-Assistance System, a search and rescue robot, an aerial drone, and an indoor robot.

19. The method of claim 14, wherein the machine-learning-based monocular depth estimator includes at least one neural network.

20. The method of claim 14, wherein the virtual image is a Red-Green-Blue (RGB) image.